Based solely on the information in this output, which of the following is the best answer? (5) The data set contains no trend or seasonality. The data set contains trend but no seasonality. The data set contains seasonality but no trend. The data set pro.
I need an explanation for this Economics question to help me study.
/0x4*
- The table below features three forecasting models used on the same set of data.
Model 1 |
Model 2 |
Model 3 |
|
Type |
Exponential Smoothing |
Regression |
Seasonal & Trend |
MSE |
8755.3 |
4876.2 |
5945.8 |
Based solely on the information in this output, which of the following is the best answer?(5)
- The data set contains no trend or seasonality.
- The data set contains trend but no seasonality.
- The data set contains seasonality but no trend.
- The data set probably contains cyclicality.
- The data set contains both trend and seasonality.
- In a forecasting application for 20 time periods, there are 10 negative errors and 10 positive errors.This indicates the model is performing well.(2)
- True
- False
- Refer to the following graph:
Which of the following apply?(8)
- The data contain a trend component.
- The data contain a seasonal component.
- The data ,contain a cyclical component.
- The data contain an irregular (random) component.
- In #3, which method (if any) is most appropriate?(4)
- Exponential smoothing.
- Regression.
- Regression with seasonal indices.
- None of the above.
- In #3, which of the following is most appropriate regarding sales?(4)
- We should use all of the data in our model.
- We should use only periods 5-16 in our model.
- We should use only periods 9-16 in our model.
- We should use only periods 13-16 in our model.
- We should use only periods 1-12 in our model.
- Refer to the Excel output on the final pages.Here, we are tracking the number of orders placed by week for a 20-week period.The first set of output is for an exponential smoothing model with α = 0.25.The second set of output is for a regression.Which of the following is most appropriate?(3)
- The exponential smoothing model is most appropriate.
- The regression is most appropriate.
- Another model would be more appropriate.
- The model with the lower MSE is always the most appropriate model.(2)
- True
- False
- In a given application, we are using regression with seasonal indices.The regression model is y = 42 + 2.5t.The seasonal indices for quarters 1-4 are 0.85, 0.92, 0.98, and 1.25, respectively.The predicted value for period 20 is ___________.(5)
- If our data contains seasonality but no trend, exponential smoothing is appropriate.(2)
- True
- False
- Annual data can exhibit seasonality.(2)
- True
- False
- We can assess quarterly seasonality with one year of data.(2)
- True
- False
Week |
Orders |
Forecast |
Error |
Error^2 |
1 |
45 |
#N/A |
#N/A |
|
2 |
56 |
45 |
11 |
121 |
3 |
65 |
47.75 |
17.25 |
297.5625 |
4 |
63 |
52.0625 |
10.9375 |
119.6289 |
5 |
54 |
54.79688 |
-0.79688 |
0.63501 |
6 |
60 |
54.59766 |
5.402344 |
29.18532 |
7 |
54 |
55.94824 |
-1.94824 |
3.795648 |
8 |
60 |
55.46118 |
4.538818 |
20.60087 |
9 |
56 |
56.59589 |
-0.59589 |
0.35508 |
10 |
57 |
56.44691 |
0.553085 |
0.305903 |
11 |
50 |
56.58519 |
-6.58519 |
43.36467 |
12 |
61 |
54.93889 |
6.06111 |
36.73706 |
13 |
47 |
56.45417 |
-9.45417 |
89.38128 |
14 |
56 |
54.09063 |
1.909375 |
3.645712 |
15 |
55 |
54.56797 |
0.432031 |
0.186651 |
16 |
52 |
54.67598 |
-2.67598 |
7.160852 |
17 |
57 |
54.00698 |
2.993017 |
8.958153 |
18 |
58 |
54.75524 |
3.244763 |
10.52849 |
19 |
61 |
55.56643 |
5.433572 |
29.52371 |
20 |
47 |
56.92482 |
-9.92482 |
98.50207 |
MSE = |
48.47673 |
SUMMARY OUTPUT |
|||||
Regression Statistics |
|||||
Multiple R |
0.139263 |
||||
R Square |
0.019394 |
||||
Adjusted R Square |
-0.03508 |
||||
Standard Error |
5.524367 |
||||
Observations |
20 |
||||
ANOVA |
|||||
|
df |
SS |
MS |
F |
Significance F |
Regression |
1 |
10.86466 |
10.86466 |
0.356001 |
0.558166112 |
Residual |
18 |
549.3353 |
30.51863 |
||
Total |
19 |
560.2 |
|||
|
Coefficients |
Standard Error |
t Stat |
P-value |
|
Intercept |
57.04211 |
2.566242 |
22.22787 |
1.54E-14 |
|
Week |
-0.12782 |
0.214226 |
-0.59666 |
0.558166 |